Improving the accuration of train arrival detection based-on vibration signal using accelerometer sensor

Suharjono, Amin; Prabowo, Yogi; Kadiran, Sri Anggraeni; Wardihani, Eni Dwi

doi:10.1063/1.5098262

Cited by 1 publication

(3 citation statements)

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“…To summarize, a review of recent literature on mode of transportation recognition performed via smart sensors yields only a small number of articles that attempted a method which was as cost-effective as ours. Most notably [26,[33][34][35], only Ref. [34] achieved the near-perfect accuracy that our results provided, and that article did not test for consistency across traffic or weather conditions as ours did.…”

Section: Vehicle Recognition Via Smart Sensorsmentioning

confidence: 49%

“…Impressively, Ref. [33] obtained its results with only accelerometer data, but it did so only by identifying train movement along railroads, and at a markedly lower accuracy than our 98%.…”

Section: Vehicle Recognition Via Smart Sensorsmentioning

confidence: 61%

“…In another study using an ADXL345 accelerometer sensor to read vibrations on railroad tracks, Ref. [33] were able to detect the arrival of trains at an average accuracy of 83%, a significant improvement over similar previous studies. Impressively, Ref.…”

Section: Vehicle Recognition Via Smart Sensorsmentioning

confidence: 70%

See 2 more Smart Citations

Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors

Pias

Eisenberg

Fresneda

2022

Sensors

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This paper explores the utilization of smart device sensors for the purpose of vehicle recognition. Currently a ubiquitous aspect of people’s lives, smart devices can conveniently record details about walking, biking, jogging, and stepping, including physiological data, via often built-in phone activity recognition processes. This paper examines research on intelligent transportation systems to uncover how smart device sensor data may be used for vehicle recognition research, and fit within its growing body of literature. Here, we use the accelerometer and gyroscope, which can be commonly found in a smart phone, to detect the class of a vehicle. We collected data from cars, buses, trains, and bikes using a smartphone, and we designed a 1D CNN model leveraging the residual connection for vehicle recognition. The model achieved more than 98% accuracy in prediction. Moreover, we also provide future research directions based on our study.

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Section: Vehicle Recognition Via Smart Sensorsmentioning

confidence: 49%

“…Impressively, Ref. [33] obtained its results with only accelerometer data, but it did so only by identifying train movement along railroads, and at a markedly lower accuracy than our 98%.…”

Section: Vehicle Recognition Via Smart Sensorsmentioning

confidence: 61%